Tonsillar Asymmetry and Malignancy: A Meta-analysis of Diagnostic Accuracy.

To investigate the diagnostic utility of asymmetrical tonsils in detecting tonsillar malignancy.

PubMed, Embase, Scopus, and Cochrane Library; from inception until December 17, 2024.

We included observational studies of adult/pediatric patients undergoing excisional tonsillectomy or incisional tonsillar biopsy that reported at least one diagnostic accuracy outcome for tonsillar asymmetry in predicting malignancy. We pooled estimates using frequentist univariate random-effects generalized linear mixed models, examined and adjusted for publication bias via visual inspection, Egger's test, and trim-and-fill, performed influence and cumulative meta-analyses, and used a Bayesian bivariate model as a sensitivity analysis. Outcome measures included the following: sensitivity, specificity, positive/negative likelihood ratio (LR+/LR-), and positive/negative predictive value (NPV/PPV) with 95% confidence interval (95% CI).

Twenty-nine studies (5178 participants) from 422 records were included. The risk of bias was low-moderate. The sensitivity and specificity of tonsillar asymmetry as a diagnostic marker for malignancy were 77.2% (95% CI: 68.6%-84.0%) and 96.4% (95% CI: 91.6%-98.6%), respectively. The LR- was 0.24 (0.17-0.34) and LR+ was 21.44 (8.05-57.0). The NPV and PPV were 99.8% (95% CI: 99.1%-99.9%) and 4.31% (95% CI: 1.83%-9.80%), without considering clinical risks. With concomitant high-risk clinical features such as lymphadenopathy, the PPV (probability of malignancy given asymmetrical tonsils) was 38.5% (30.3%-47.4%). Without other high-risk features, the PPV was 0.16% (0.15%-0.18%). The overall quality of evidence was high.

Tonsillar asymmetry has a high specificity and moderate sensitivity for tonsillar malignancy. Due to the low prevalence of malignancy, the probability of malignancy is less than 1% if no other suspicious clinical features are present.
Cancer
Care/Management

Authors

Gao Gao, See See, Tan Tan, Chan Chan, Zhou Zhou, See See, Kiong Kiong, Chan Chan, Koh Koh, Tan Tan, Abhilash Abhilash, Lim Lim
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